Marketing Research Methods Archives

Building a StoryBrand 2.0: Clarify Your Message so Customers Will Listen.<\/a><\/p>\n\n\n\nStory is the greatest weapon you have to combat marketing noise because it organizes information in such a way that people are compelled to listen. If you want to bring attention to your brand, you must understand how story works and how to invite customers into a narrative that is compelling.<\/p>\n\n\n\nHere is nearly every story you watch, read, or hear in a nutshell: A character<\/em><\/strong> who wants something encounters a problem<\/em><\/strong> before they can get it. At the peak of their despair, a guide<\/em><\/strong> steps into their lives, gives them a plan<\/em><\/strong>, and calls them to action<\/em><\/strong>. That action helps them avoid failure<\/em><\/strong> and experience a success<\/em><\/strong>.<\/p>\n\n\n\nThat\u2019s really it. You\u2019ll see some form of this structure in every movie you watch, every novel you read, and every story you hear from this moment forward.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\nhttps:\/\/youtu.be\/bDkUaR-N7N8?feature=shared\n<\/div><\/figure>\n\n\n\nBy understanding this StoryBrand formula<\/strong>, you can essentially predict what is going to happen in almost every story you encounter. These seven basic plot points are powerful because they work to hold a human being\u2019s attention. That\u2019s why that formula has been used in countless stories for thousands of years. However, this formula is not stale or tiresome. In fact, these plot points are like chords of music in the sense that you can use them to create an infinite variety of narrative expression.<\/p>\n\n\n\nWhat does any of this have to do with growing your business? Everything. The same rules that get and keep a movie audience\u2019s attention can also get and keep a customer\u2019s attention. And attention is what you need more than anything else.<\/p>\n\n\n\nAt StoryBrand, our certified coaches have reviewed thousands of pages of marketing copy that had nothing to do with the customer\u2019s story. We tell our clients the same thing my filmmaker friends told me when I was writing screenplays: Anything that doesn\u2019t serve the plot has to go. Just because a tagline sounds great or a picture on a website grabs the eye doesn\u2019t mean it helps us enter into our customers\u2019 story. In every line of marketing and messaging copy we write, we\u2019re either serving the customer\u2019s story or descending into confusion; we\u2019re either making music or making noise.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=https://www.ama.org/"https:////www.amazon.com//Building-StoryBrand-2-0-Clarify-Customers//dp//1400248876?maas=maas_adg_0816D1BAD3D457D8B7B2F9ADA7B3255A_afap_abs&ref_=aa_maas&tag=maas]\%22> $\"\"$ Building a StoryBrand 2.0<\/a><\/em>, we\u2019ve expanded its capability. We\u2019ve paired the new book with <a href=https://www.ama.org/"https:////storybrand.com//?utm_source=american-marketing-association&utm_medium=email&utm_campaign=hcl-ad\%22>StoryBrand.ai<\/a><\/strong>, a tool that will let you generate better headlines and sound bites\u2014and expand them into full messaging and marketing campaigns you can use to invite customers into a story. StoryBrand.ai encompasses all matter of messaging and marketing, including websites, keynote addresses, lead generators, digital and traditional advertising, and even casual conversation. StoryBrand.ai is completely free for anyone who purchases the book.<\/p>\n\n\n\nOnce we begin filtering our message through the StoryBrand framework and using it as a communication filter, we will be able to repeat powerful messages over and over that \u201cbrand\u201d us into our customers\u2019 lives.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=https://www.ama.org/"https:////storybrand.com//?utm_source=american-marketing-association&utm_medium=email&utm_campaign=hcl-ad]\%22> $\"\"$ Consumer Rational (In)Attention to Favorable and Unfavorable Product Information, and Firm Information Design<\/a>.\u201d<\/p>\n\n\n\nConsumer big data provides marketers with information about every touchpoint in a consumer\u2019s journey, including the feedback after the actual purchase. Using artificial intelligence, marketers conduct multiple analyses to provide targeted information to consumers regarding various facts about the product, like its pricing, attributes, and other information that can enable a consumer to make an effective decision. Marketers also have the responsibility of providing factual yet persuasive information that results in a beneficial transaction to both consumers and the firms. The authors have found that strategically placing the information in ways that resonate with consumers\u2019 own beliefs, such that it confirms both the positive and negative information they have about a product, may result in more purchases. Contrary to intuitive belief, the authors suggest that providing negative information about the product along with its positives thus becomes advantageous to the firms. The authors build a mathematical model that illustrates the benefits of providing these two types of information for both marketers and consumers.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\"> The authors have found that strategically placing the information in ways that resonate with consumers\u2019 own beliefs, such that it confirms both the positive and negative information they have about a product, may result in more purchases. <\/p><\/blockquote>\n\n\n\nThe authors construct their framework based on \u201crational inattention theory,\u201d which states that when information processing is costly, consumers optimally process only a part of it. Using this as the theoretical basis for the model, the authors provide a mathematical solution that optimizes the consumers\u2019 attention allocation toward both favorable and unfavorable product information. Research in psychology has found that people tend to focus on information that affirms their beliefs (confirmation bias), and the authors show that this may occur for consumers when information processing is costly. Also, the authors show that marketers may benefit from both favorable and unfavorable product information, challenging the intuition that sellers cannot profit from negative information of the product. The negative information becomes essential to consumers especially when deciding the fit of the product to their needs, and if a sufficient amount of negative information is not available, then consumers may not even start their search process.<\/p>\n\n\n\nQ: The application of mathematical modeling using confirmation bias is very interesting. What aspects of the modeling did you enjoy the most? Why?<\/strong><\/strong><\/p>\n\n\n\nA: We had an open-ended research question that motivated us. We wanted to understand what information consumers seek when they are making purchase decisions. I think the enjoyable part was the emergence of the phenomenon known as confirmation bias as the result of optimal consumer information processing behavior. This confirmation bias can be a reason why the firm should provide unfavorable information about the product: if you as a consumer know that the firm gives only positive information and will never give me any negatives about the product, you are likely to feel that you will not be able to make a confident decision using this information. If you feel that the information given is not going to help you make a decision, you might not search for any information at all. In addition, if you don\u2019t search for information, you will not be confident about buying the product. Therefore, the availability of negative or unfavorable information can induce purchases. <\/p>\n\n\n\nQ: What types of websites in your opinion would benefit from the information design? Are there any websites that are exempt from this framework? Was there any particular website you were thinking of when developing this research?<\/em><\/strong><\/em><\/strong><\/p>\n\n\n\nA: The framework would be applicable to most e-commerce websites. When consumers have a product in mind, they seek information. They can visit e-commerce websites like Amazon, where they can find the product ratings and can sort by the star rating. When you click on the 5-star link you will see all the 5-star reviews, or you click on the one-star link, and you see all the one-star reviews. I was doing the same to see what unfavorable information was there on Amazon. Essentially, e-commerce websites like Amazon were used as a frame of reference where primarily information gathering is important. However, if it is a discussion forum, where the objective of the website or the person who runs the forum is just to provide information and it is not easy to sort this information as positive or negative, then this model is less relevant. Some examples include Wikipedia or Quora. <\/p>\n\n\n\nQ: The current framework studies many interesting concepts like confirmation bias, attention allocation, the valence of information. What would you say were some of the challenges you faced while developing this research?<\/em><\/strong><\/em><\/strong><\/p>\n\n\n\nA: There were many challenges. We wanted a strong theoretical foundation that would be supported by data. One reason I like this paper is that it starts off with the idea that consumers are rational, but it ends up showing that they can do things that might appear irrational, like confirmatory search or confirmation bias. Most people know and understand that consumers are not fully rational, right? If you take a rational consumer, they incur a reasonable cost, like the cost of thinking or the cost of processing information, then you can see the behavior as predicted by our framework. This action is essentially rational but appears irrational. Putting it all together in a framework that is theoretically solid, intuitive, and yet technically solvable was a challenge, but it was rewarding.<\/p>\n\n\n\nQ: How would you think information design would change with respect to using AI devices. What are some of the aspects to think about in this scenario?<\/em><\/strong><\/em><\/strong><\/p>\n\n\n\nA: It depends on how it is used. One aspect is that you can use AI systems on the fly. I think AI can cut both ways. For example, a person could be looking at certain information and the AI makes suggestions which is the next best piece of information, helping the consumer make a good decision. However, you could also have it the other way around where the consumer has seen certain information and AI gives them a different piece of information or makes it easier to search, increasing the chance that they end up buying the product. AI is a big tool that firms can use for consumers\u2019 benefit. In order to be used for consumers\u2019 rather than just for the firms\u2019 benefit, there must be some regulations around that.<\/p>\n\n\n\nQ: Visuals and graphics form a large portion of website information. What are your thoughts about including visual information? How would this change the framework?<\/em><\/strong><\/em><\/strong><\/p>\n\n\n\nA: Visuals can really help with attributes that otherwise can be difficult to understand. Think of a car. Visually I can decide, I like red more than blue. Visuals can help a lot in conveying information when it is about softer match attributes or information on horizontal attributes. Information on vertical attributes is often information that can be digitally conveyed well in text or in some other numerical format. Visual information is a very good complement to textual information. Even complicated information can be given visually to make it is easier to understand. Sometimes it can be \u201cin place of\u201d textual information and sometimes it can be \u201ccomplementary.\u201d<\/p>\n\n\n\nQ. We were also curious to know how marketers can avoid negative perceptions arising from information management.<\/em><\/strong><\/em><\/strong><\/p>\n\n\n\nA: This is a big debate going on in privacy circles, which is all about information. Consumers are worried that there is a lot of information that marketers have. It\u2019s a very important issue and it is a matter of trust. I trust some websites more than others. It's about reputation. Coming back to this paper and talking about favorable and unfavorable information, there should be some of both. First of all, as a marketer, you should be giving different kinds of information and not make it too difficult to understand or find information. This also helps with reducing negative perceptions. Over time a firm will build the reputation of being fair. Marketers should not make finding unfavorable information especially hard. They can always highlight the positive things, but I think marketers should make unfavorable information readily available as well.<\/p>\n\n\n\nRead the full article:<\/strong><\/p>\n\n\n\nJerath Kinshuk, and Qitian Ren (2021), \"<a href=https://www.ama.org/"https:////journals.sagepub.com//doi//abs//10.1177//0022243720977830/" target=\"_blank\" rel=\"noreferrer noopener\">Consumer Rational (In)Attention to Favorable and Unfavorable Product Information, and Firm Information Design<\/a>,\" Journal of Marketing Research<\/em>. 58 (2), 343\u201362. doi:10.1177\/0022243720977830<\/p>\n","post_title":"Do You Think Your Decision to Buy Was Rational?","post_excerpt":"AMA Doctoral SIG members interview recent JMR authors about their research on consumers rational inattention, which has some surprising findings. ","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"do-you-think-your-decision-to-buy-was-rational","to_ping":"","pinged":"","post_modified":"2024-01-03 17:04:21","post_modified_gmt":"2024-01-03 23:04:21","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=99888","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":96978,"post_author":"185","post_date":"2022-03-16 05:02:00","post_date_gmt":"2022-03-16 05:02:00","post_content":"\nQuasi-experimental methods have been widely applied in marketing to explain changes in consumer behavior, firm behavior, and market-level outcomes. The purpose of quasi-experimental methods is to determine the presence of a causal relationship in the absence of experimental variation. A <a href=https://www.ama.org/"https:////doi.org//10.1177//00222429221082977/" target=\"_blank\" rel=\"noreferrer noopener\">new Journal of Marketing <\/em>article<\/a> offers guidance on how to successfully conduct research in marketing with quasi-experiments to understand whether an action causally affects a marketing outcome.<\/p>\n\n\n\nAs a vivid example, we describe a quasi-experiment that occurred when eBay shut down all the paid search advertising on Bing during a dispute with Microsoft, but lost little traffic. These quasi-experimental results inspired a follow-up field experiment where eBay randomized suspension of its branded paid search advertising and found results consistent with the quasi-experiment. We begin by establishing various type of quasi-experimental variation at the individual, organizational, and market-levels. In each type, given the lack of an experiment, some individuals, companies, or markets receive an action or policy (i.e., treatment group) and some do not (i.e., control group). For example, some markets are affected by a new policy and some are not. The question is how the markets receiving the treatment would act if they had not received it (i.e., the counterfactual). The unobservability of the counterfactual means assumptions are required to ensure that differences (both observed and unobserved) are as untroubling as possible, thereby mimicking random assignment as closely as possible. <\/p>\n\n\n\nWe discuss how to structure an empirical strategy to identify a causal relationship using methods such as difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. We emphasize the importance of clearly communicating identifying assumptions underlying the assertion of causality and establishing the generalizability of the findings.<\/p>\n\n\n\nWe examine the following topics with the goal of helping researchers and analysts use quasi-experiments more effectively.<\/p>\n\n\n\n<ul class=\"is-style-featured\">\n<li>Topic 1 - Research question:<\/strong> Do we care whether x causes y? The first and hardest stage in this process is identifying a question in which marketing scholars, managers, or policy makers actually care whether x causes y.<\/li>\n\n\n\n<li>Topic 2 - Data question:<\/strong> How to find data with quasi-experimental variation in x? Much of the work using quasi-experimental variations in marketing harnesses easily understood events such as contract changes; ecological issues such as the weather; geography; and macroeconomic, individual, organizational, and regulatory changes. The key is to consider why each of these sources of variation can approximate random assignment.<\/li>\n\n\n\n<li>Topic 3 - Identification strategy:<\/strong> Does x cause y to change? The researcher must first explain where the variation claimed to be exogenous comes from. Second, the researcher needs to demonstrate that the relationship between the variation and the outcome of interest is very likely driven by the relationship between x and y and not by some other factor.<\/li>\n\n\n\n<li>Topic 4 - Empirical analysis:<\/strong> How to estimate the effect of x on y? We discuss three different regression analysis frameworks using quasi-experiments: difference-in-differences, regression discontinuity, and instrumental variables.<\/li>\n\n\n\n<li>Topic 5 - Challenges to research question:<\/strong> What if variation in x is not exogenous? We outline three methods\u2014propensity score matching, synthetic control methods, and selection bias correction\u2014with steps to take when comparability between the control and treatment groups is violated.<\/li>\n\n\n\n<li>Topic 6 - Robustness:<\/strong> How robust is the effect of x on y? The idea here is to show that the sign, significance, and magnitude of the estimate remain broadly consistent across a vast range of possible models. A few examples of robustness checks include different controls, functional forms, choices of time periods, dependent variables, the size of the control group, and a placebo test.<\/li>\n\n\n\n<li>Topic 7 - Mechanism:<\/strong> Why does x cause y to change? Understanding how the process of the change unfolds adds insight that can drive new knowledge and stronger actions.<\/li>\n\n\n\n<li>Topic 8 - External validity:<\/strong> How generalizable is the effect of x on y? The external validity discussion in a paper should recognize the assumptions required for the analysis to capture the average treatment effect across the population of interest rather than a more local effect that is an artifact of the data sample or the source of quasi-experimental variation.<\/li>\n\n\n\n<li>Topic 9 - Apologies:<\/strong> What remains unproven and what are the caveats? Any identification strategy relies on assumptions that need to be explicit throughout the paper. While apologies do not mean all is forgiven, the objective should be to clarify the boundaries of claims made in the paper.<\/li>\n<\/ul>\n\n\n\n<a href=https://www.ama.org/"https:////doi.org//10.1177//00222429221082977/" target=\"_blank\" rel=\"noreferrer noopener\">Read the full article<\/a>.<\/p>\n\n\n\nFrom: <\/strong>Avi Goldfarb, Catherine Tucker, and Yanwen Wang, \u201c<a href=https://www.ama.org/"https:////doi.org//10.1177//00222429221082977/" target=\"_blank\" rel=\"noreferrer noopener\">Conducting Research in Marketing with Quasi-Experiments<\/a>,\u201d <a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/em><\/a>.<\/p>\n\n\n\nGo to the <a href=https://www.ama.org/"https:////www.ama.org//journal-of-marketing///" target=\"_blank\" rel=\"noreferrer noopener\">Journal of Marketing<\/em><\/a><\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n","post_title":"The Benefits of Quasi-Experiments in Marketing [Research Methodology]","post_excerpt":"What can marketers do if they can\u2019t run a field experiment?","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"conducting-research-in-marketing-with-quasi-experiments","to_ping":"","pinged":"","post_modified":"2024-07-10 06:59:41","post_modified_gmt":"2024-07-10 11:59:41","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=96978","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":77045,"post_author":"20","post_date":"2021-04-05 16:48:17","post_date_gmt":"2021-04-05 16:48:17","post_content":"\n<a href=https://www.ama.org/"https:////shidler.hawaii.edu//mkt//directory//sungjin-kim/" target=\"_blank\" rel=\"noreferrer noopener\">Sungjin Kim<\/a>, <a href=https://www.ama.org/"https:////www.johnson.cornell.edu//faculty-research//search///" target=\"_blank\" rel=\"noreferrer noopener\">Clarence Lee<\/a>, and <a href=https://www.ama.org/"https:////www.johnson.cornell.edu//faculty-research//faculty//sg248///" target=\"_blank\" rel=\"noreferrer noopener\">Sachin Gupta<\/a> have been selected to receive the <a href=https://www.ama.org/"https:////www.ama.org//paul-e-green-award///" target=\"_blank\" rel=\"noreferrer noopener\">Paul E. Green Award<\/a> for their article \"<a href=https://www.ama.org/"https:////journals.sagepub.com//doi//full//10.1177//0022243720936230/" target=\"_blank\" rel=\"noreferrer noopener\">Bayesian Synthetic Control Methods<\/a>,\" which appeared in the October issue (Volume 57, No. 5) of Journal of Marketing Research<\/em> (JMR<\/em>).<\/p>\n\n\n\nThe <a rel=\"noreferrer noopener\" href=https://www.ama.org/"https:////www.ama.org//paul-e-green-award///" target=\"_blank\">Paul E. Green Award<\/a> recognizes the article in JMR<\/em> that demonstrates the greatest potential to contribute to the theory, methods, and practice of marketing. It honors the S.S. Kresge Professor Emeritus of Marketing at the Wharton School at the University of Pennsylvania. On behalf of the AMA, this year\u2019s Green Award selection process was managed by Professor Michel Wedel<\/strong>, Distinguished University Professor at the Robert H. Smith School of Business at the University of Maryland. The committee overseeing the selection process consisted of Russ Winer <\/strong>(New York University), Kusum Ailawadi <\/strong>(Dartmouth College), <\/strong>and Mary Frances Luce<\/strong> (Duke University). \u200bIn recognizing the winning paper, the committee made the following comment:<\/p>\n\n\n\nIn their paper, \u201cBayesian Synthetic Control Methods,\u201d Kim et al. significantly improve the application of synthetic control methods (SCMs) to marketing and other social science problems where the lack of a randomized control group inhibits the ability to estimate the treatment effect. SCMs are very useful tools for such quasi-experimental research that creates a \u201csynthetic\u201d control unit as a weighted average of a set of controls where the weights are determined by getting as close as possible to the pre-treatment outcome in the treatment unit. It has three limitations: restrictive constraints on the weights, no formal theory for statistical inference, and what is called the \u201clarge p, small n\u201d sparsity problem, in which there are more parameters than observations.<\/em><\/p>\n\n\n\nThe significant contribution of this paper is the use of Bayesian methods to address these three limitations. It relaxes the constraints on the weights, provides exact statistical inference, and uses shrinkage priors to solve the sparsity problem. In addition, the authors\u2019 methods incorporate analogs of the frequentist SCM variants used in prior research. The authors provide computer code for their SCM approach so that it can be implemented by practitioners and other academics. With respect to the practical applications, the example in the paper of the impact of a soda tax imposed on Washington State consumers in 2010 illustrates how their method can be applied in the real world. Thus, the paper makes important advances both methodologically and substantively as it gives analysts a new tool to improve the measurement of the causal effects of a variety of marketing, policy, and other interventions where randomized controlled tests are either infeasible or expensive.<\/em><\/p>\n\n\n\nIn addition to the winning paper, the three other excellent finalists for the award were as follows:<\/p>\n\n\n\n<div class=\"list-block\" style=\"color:#201c0f;font-size:21px\"><ul><li>Verena Schoenmueller, Oded Netzer, and, Florian Stahl, \"<a href=https://www.ama.org/"https:////journals.sagepub.com//doi//full//10.1177//0022243720941832/" target=\"_blank\" rel=\"noreferrer noopener\">The Polarity of Online Reviews: Prevalence, Drivers and Implications<\/a>,\" Vol. 57, No. 5<\/li><li>Anocha Aribarg and Eric M. Schwartz, \"<a href=https://www.ama.org/"https:////journals.sagepub.com//doi//full//10.1177//0022243719879711/" target=\"_blank\" rel=\"noreferrer noopener\">Native Advertising in Online News: Trade-Offs Among Clicks, Brand Recognition, and Website Trustworthiness<\/a>,\" Vol. 57, No. 1<\/li><li>Ryan Dew, Asim Ansari, and Yang Li, \"<a href=https://www.ama.org/"https:////journals.sagepub.com//doi//full//10.1177//0022243719874047/" target=\"_blank\" rel=\"noreferrer noopener\">Modeling Dynamic Heterogeneity Using Gaussian Processes<\/a>,\" Vol. 57, No. 1 <\/li><\/ul><\/div>\n","post_title":"Kim et al. Receive Journal of Marketing Research 2020 Green Award","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"kim-et-al-receive-journal-of-marketing-research-2020-green-award","to_ping":"","pinged":"","post_modified":"2024-01-08 14:43:55","post_modified_gmt":"2024-01-08 20:43:55","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.ama.org\/?p=77045","menu_order":0,"post_type":"post","post_mime_type":"","comment_count":"0","filter":"raw"}]" />

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The Benefits of Quasi-Experiments in Marketing [Research Methodology]

Kim et al. Receive Journal of Marketing Research 2020 Green Award